I can see how LLMs can help with testing, but one should never compare LLMs with deterministic tools like compilers. LLMs are entirely a separate category.

It’s not “economizing” if I have to verify every test myself. To actually validate that tests are good I need to understand the system under test, and at that point I might as well just write the damn thing myself.

This is the fundamental problem with this “AI” mirage. If I have to be an expert to validate that the LLM actually did the task I set out, and isn’t just cheating on tests, then I might as well code the solution myself.

From a PM perspective, the main differentiator between an engineering team and AI is "common sense". As these tools get used more and more, enough training data will be available that AI's "common sense" in terms of coding and engineering decisions could be indistinguishable from a human's over time. At that point, the only advantage a human has is that they're also useful on the ops and incident response side, so it's beneficial if they're also comfortable with the codebase.

Eventually these human advantages will be overcome, and AI will sufficiently pass a "Turing Test" for software engineering. PMs will work with them directly and get the same kinds of guidance, feedback, documentation, and conversational planning and coordination that they'd get from an engineering team, just with far greater speed and less cost. At that point, yeah you'll probably need to keep a few human engineers around to run the system, but the system itself will manage the software. The advantage of keeping a human in the loop will dwindle to zero.